Jove
Visualize
Contact Us

Related Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Eye Melanoma Diagnosis System using Statistical Texture Feature Extraction and Soft Computing Techniques.

Journal of biomedical physics & engineering·2023
Same author

Prototype-Incorporated Emotional Neural Network.

IEEE transactions on neural networks and learning systems·2017
Same author

Banknote recognition: investigating processing and cognition framework using competitive neural network.

Cognitive neurodynamics·2017
Same author

Disk hernia and spondylolisthesis diagnosis using biomechanical features and neural network.

Technology and health care : official journal of the European Society for Engineering and Medicine·2016
Same author

Modeling cognitive and emotional processes: a novel neural network architecture.

Neural networks : the official journal of the International Neural Network Society·2010
Same author

A neural network model for credit risk evaluation.

International journal of neural systems·2009
Same journal

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

International journal of neural systems·2026
Same journal

Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images.

International journal of neural systems·2026
Same journal

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

International journal of neural systems·2026
Same journal

Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer.

International journal of neural systems·2026
Same journal

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

International journal of neural systems·2026
Same journal

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

International journal of neural systems·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 28, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Blood cell identification using a simple neural network.

Adnan Khashman1

  • 1Electrical & Electronic Engineering Department, Near East University Lefkosa, Mersin, Turkey. amk@neu.edu.tr

International Journal of Neural Systems
|November 11, 2008
PubMed
Summary
This summary is machine-generated.

Automating blood cell identification using a novel system with neural networks offers a fast and efficient alternative to manual methods. This approach enhances accuracy and speed for laboratory reporting.

More Related Videos

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
09:31

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Related Experiment Videos

Last Updated: Jun 28, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
09:31

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Area of Science:

  • Hematology
  • Computer Science
  • Biomedical Engineering

Background:

  • Manual classification of blood cell types is labor-intensive and prone to errors.
  • Accurate blood cell identification is crucial for clinical diagnostics and laboratory automation.

Purpose of the Study:

  • To develop and evaluate an automated blood cell identification system.
  • To simulate human visual inspection for classifying three main blood cell types.
  • To compare the performance of two different neural network models for this task.

Main Methods:

  • Feature extraction using global pattern averaging.
  • Classification using two distinct neural network architectures.
  • Comparative analysis of the investigated neural networks.

Main Results:

  • The proposed system demonstrates fast, simple, and efficient blood cell identification.
  • Experimental results indicate the system's viability for automating laboratory reporting.
  • The study provides a comparative performance analysis of the two neural networks.

Conclusions:

  • The developed blood cell identification system effectively automates a critical laboratory process.
  • Neural network-based approaches offer a promising solution for accurate and rapid blood cell classification.
  • This technology has the potential to significantly improve laboratory workflow and reporting efficiency.